1. Field of the Invention
The present invention relates to the field of signal processing, and in particular, to signal processing in a read channel.
2. Background
Digital transmission is often performed through analog channels. Digital information is transmitted over the analog channel in the form of a "symbol" representing a digital value. In some cases, adjacent symbols can overlap, resulting in a phenomenon known as intersymbol interference. This interference can corrupt digital transmissions, leading to errors in the receipt of the digital information. An efficient mechanism for optimizing the channel is needed.
Partial response signaling allows for better handling of intersymbol interference as well as more efficient utilization of the bandwidth of a given channel. In partial response systems, a controlled amount of intersymbol interference can be allowed. The partial response system is described by the polynomials 1+D, 1-D and (1-D.sup.2), also called duobinary, dicode, and class-IV, respectively.
Precoding is often performed to take full advantage of partial response signaling. With preceding, a method is required for decoding the binary symbol sequence that is output from the channel in its corrupted form. For example, in a magnetic recording channel, maximum likelihood sequence estimation (MLSE) decoding, in conjunction with partial response signaling systems, can be used as an effective tool in pulse detectors for receiving and decoding digital transmissions that suffer from intersymbol interference.
For example, class IV partial response waveforms are formed by the subtraction of binary waveforms two bit intervals apart. This process boosts midband frequencies making the system more immune to noise and distortion at both high and low frequencies. This is especially useful in a magnetic recording channel where, using a conventional inductive head, there is little signal at low frequencies and spacing losses can cause large attenuation at high frequencies.
Because class IV partial response signaling for digital detection is especially suited for the magnetic recording channel, sampled amplitude detection can be applied for magnetic recording. To minimize the propagation of data errors, the signal is turned into a sequence of binary numbers. Procedures for determining the maximum likelihood sequence in the presence of noise can then be applied. With sequence detection, sequences of bits are detected and processed to minimize error.
In a digital magnetic recording system, information bits are recorded on a medium using two stable states of magnetization. For example, using the NRZ (Non-return-to-Zero) recording method, magnetization pattern m(t) can be created as shown in FIG. 15. m(t) can be represented as ##EQU1## where u(t) is a rectangular pulse of duration T: ##EQU2## and the coefficients a.sub.k represent the binary magnetization level from time kT to time kT+T: ##EQU3##
In reading back the recording pattern, the output voltage e(t) is given by: ##EQU4## where h(t) represents the magnetic field response to a unit step function and * represents a convolution operation. Combining equations (1) and (2) yields: ##EQU5## x.sub.k can have three different levels of -1, 0, or +1 (note: -1, 0 and +1 are symbolic voltage levels, not actual voltage values). The x.sub.k sequence, however, is not generated nor observed in the recording system.
An example of a PRML (Partial Response Maximum Likelihood) read channel for processing the signal e(t) is shown in FIG. 12. The read signal e(t) is provided to AGC (automatic gain control) amplifier 1100 via input 1108. The gain of AGC amplifier 1100 is controlled via signal 1111. Amplified output 1109 of AGC amplifier 1100 is provided to programmable filter/equalizer 1101 to shape the read signal waveform into the desired target waveform. Filter/equalizer 1101 has adjustable filter tap coefficients controlled via lines 1107 for modifying the filter/equalizer transfer function. The filtered data signal 200 from filter/equalizer 1101 is provided to PRML/Viterbi Detector 1200, and subsequently to decoder 1201, for detection of the sequence x.sub.k and reconstruction of the digital information.
AGC amplifier 1100 and filter/equalizer 1101 are adjusted so that data signal 200 more closely approximates the waveform expected by detector 1200. Thus, the accuracy of detector 1200 is dependent on the efficient tuning of AGC amplifier 1100 and filter/equalizer 1101.
The read signal e(t) of FIG. 15 is sampled and quantized to generate raw data samples of the x.sub.k sequence. Because the x.sub.k sequence likely contains errors, and the retrieval of the recorded information requires accurate detection of pulse sequences, maximum likelihood sequence estimation (MLSE) techniques, such as the Viterbi algorithm, are used in detector 1200 to improve the detection of symbol (pulse) sequences in the presence of noise and intersymbol interference. The x.sub.k sequence is fed to a decoding stage, such as decoder 1201, to retrieve the original information as accurately as possible.
In one embodiment of Viterbi decoding, the received data is sampled and quantized to three-bit accuracy, and path metric calculations are performed on the data using digital electronics. Typically, data is not decoded as soon as it is received. Instead, a sequence of data following the digit to be decoded is first collected. By computing path metrics (the accumulated log likelihood), a limited number of possible sequences are identified with one survivor sequence ending in each of the data states. The highest correlated of the survivor sequences is selected to be the sole survivor sequence. However, for an ML sequence estimator or a Viterbi detector to accurately reproduce the original information, the x.sub.k sequence needs to be sampled from the incoming analog signal waveform at precise locations.
An MLSE detection technique such as the Viterbi algorithm is used because the transmission path or channel typically introduces transmission errors and corrupts the original data when the coded information is transmitted from a source to a destination. The various ways in which digital data can be conveyed make it more difficult to identify and compensate for errors because of the many different error mechanisms associated with them.
For example, in magnetic recording, error sources can range from mechanical problems such as poor read/write head contact and flux density variations in a disk drive to Gaussian noise in replay circuits and heads. Whether it is a mechanical problem or Gaussian thermal noise, the result is corrupted data on the receiving side that is not the same as the originally transmitted data.
These errors stemming from various causes can be compensated to minimize detection errors at the output of a Viterbi detector by adjusting some front end parameters including those in the AGC, equalization, sampling arid quantization stages. Fine tuning a whole read channel system, however, can include adjusting hundreds of parameters. Fine tuning a disk drive system for even lower error rates may involve adjusting more than two thousand parameters.
FIG. 13 is a block diagram of a prior art read channel monitor system using the MSE (Mean Square Error) technique for PRML systems. Since PRML read channel systems are often implemented in analog circuitry, the MSE signal of the input data samples is also analog. The analog MSE implementation shown in FIG. 13 for a PRML sampled read channel comprises differential subtractor 1305 and gain stage 1307 to obtain the difference between equalizer 1101 output and the target Viterbi sampled value 1303. An analog multiplier 1309 coupled to gain stage 1307 performs the squaring of the error signal.
The outputs of multiplier 1309 are fed to an on-chip integrator 1311 to obtain an analog MSE signal. The differential analog MSE signal is then brought off-chip via a differential analog output buffer 1313 to an off-chip filter 1315. The filtered MSE signal goes through a differential to single-ended analog conversion before digitizing by the servo A/D and further processing by DSP microprocessor 1319.
The MSE scheme of FIG. 13 is prone to error due to poor signal-to-noise ratio (SNR) caused by the small voltage difference between the sample and the target value, typically down to a few millivolts as the data samples come close to the target. The analog summing and multiplying stages add additional noise and offset to the MSE signal, making it difficult to filter, and the long analog MSE signal path is susceptible to degradation from other on-chip noise sources such as clocks and digital signal lines. Also, the SNR of the analog MSE decreases as the channel approaches ideal equalization, which decreases the accuracy of the MSE measurement at the very point where it is desired to have the system operate.
Experiments using an SSI 4910 PRML channel, manufactured by Silicon Systems, Inc., verify that the MSE method has relatively low sensitivity and does not provide a single optimum solution for the continuous time filter cutoff and boost. As can be inferred from FIG. 13, the analog MSE solution is hardware-intensive with both on-chip and off-chip components.
An alternative approach to the analog MSE solution is to use a high-resolution analog-to-digital converter (ADC) to quantize the incoming signal, and perform digital operations of summing, multiplying, and averaging using hardware. This, however, is also a hardware-intensive solution, especially with the need for a high resolution ADC.
Viterbi Threshold Marginalization techniques can be used to optimize the PRML read channel, as suggested by Z. Keirn, et al. in "A Window-Margin Like Procedure . . . ", IEEE Transactions. on Magnetics, 3/95. Using this technique, the Viterbi threshold can be increased about 50% to marginalize the channel performance, and optimize the channel for 10.sup.-6 BER (bit error rate) performance by mapping the BER contours with respect to the parameters in question. The optimized setting maps to a 10.sup.-9 range BER once the Viterbi threshold is returned to normal. The lower BER target reduces the tuning time of the hard disk drive, resulting in cost reduction in manufacturing.
However, the threshold marginalization technique of Kiern et al. interferes with the normal operation of the channel, and requires external custom software and hardware to process the data. It is also known that Viterbi marginalization is not always an accurate predictor with respect to the channel BER, and does not always map to the lowest MSE and BER.
Another channel marginalization technique is to add white Gaussian noise to the input signal as shown in FIG. 14, degrading the input SNR. This method is used in some advanced PRML systems by summing in the output (1401) from a programmable noise generator (1400) before the equalization step. In FIG. 14, the noise generator output 1401 is summed with the input signal 1108 in AGC amplifier 1100. The amplified input signal and noise are provided to filter/equalizer 1101, and, hence, the rest of the channel, via line 1109. By adding noise to the system and optimizing the channel for a 10.sup.-6 range BER, the drive tune time can be reduced. The noise generator, however, disturbs the normal analog signal path and the operation of the read channel, and the effects of channel tuning may not have a one-to-one mapping once the noise source is removed from the read channel.
Further, in this noise generator method, the summing circuit and the noise generator can be a source of error themselves, and may degrade the performance of the channel during normal operations. Additional external hardware and software are also required to extract the BER information from the raw channel output data, similar to Viterbi threshold marginalization.
Alternatively, the MSE of the sampled data can be used in combination with the noise generator for optimization. But again, the drawbacks of the analog MSE detract from the performance of such a system.